Model Oriented Design Of Experiments PDF Download
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Author | : Valerii V. Fedorov |
Publisher | : Springer Science & Business Media |
Total Pages | : 120 |
Release | : 2012-12-06 |
Genre | : Mathematics |
ISBN | : 1461207037 |
Download Model-Oriented Design of Experiments Book in PDF, ePub and Kindle
Here, the authors explain the basic ideas so as to generate interest in modern problems of experimental design. The topics discussed include designs for inference based on nonlinear models, designs for models with random parameters and stochastic processes, designs for model discrimination and incorrectly specified (contaminated) models, as well as examples of designs in functional spaces. Since the authors avoid technical details, the book assumes only a moderate background in calculus, matrix algebra, and statistics. However, at many places, hints are given as to how readers may enhance and adopt the basic ideas for advanced problems or applications. This allows the book to be used for courses at different levels, as well as serving as a useful reference for graduate students and researchers in statistics and engineering.
Author | : Alessandra Giovagnoli |
Publisher | : Springer Science & Business Media |
Total Pages | : 259 |
Release | : 2010-06-10 |
Genre | : Mathematics |
ISBN | : 3790824100 |
Download mODa 9 – Advances in Model-Oriented Design and Analysis Book in PDF, ePub and Kindle
Statisticians and experimentalists will find the latest trends in optimal experimental design research. Some papers are pioneering contributions, leading to new open research problems. It is a colection of peer reviewed papers.
Author | : Valerii V Fedorov |
Publisher | : |
Total Pages | : 132 |
Release | : 1997-06-01 |
Genre | : |
ISBN | : 9781461207047 |
Download Model-Oriented Design of Experiments Book in PDF, ePub and Kindle
Author | : Douglas C. Montgomery |
Publisher | : Wiley |
Total Pages | : 0 |
Release | : 2005 |
Genre | : Experimental design |
ISBN | : 9780471661597 |
Download Design and Analysis of Experiments Book in PDF, ePub and Kindle
This bestselling professional reference has helped over 100,000 engineers and scientists with the success of their experiments. The new edition includes more software examples taken from the three most dominant programs in the field: Minitab, JMP, and SAS. Additional material has also been added in several chapters, including new developments in robust design and factorial designs. New examples and exercises are also presented to illustrate the use of designed experiments in service and transactional organizations. Engineers will be able to apply this information to improve the quality and efficiency of working systems.
Author | : Thomas J. Santner |
Publisher | : Springer |
Total Pages | : 436 |
Release | : 2019-01-08 |
Genre | : Mathematics |
ISBN | : 1493988476 |
Download The Design and Analysis of Computer Experiments Book in PDF, ePub and Kindle
This book describes methods for designing and analyzing experiments that are conducted using a computer code, a computer experiment, and, when possible, a physical experiment. Computer experiments continue to increase in popularity as surrogates for and adjuncts to physical experiments. Since the publication of the first edition, there have been many methodological advances and software developments to implement these new methodologies. The computer experiments literature has emphasized the construction of algorithms for various data analysis tasks (design construction, prediction, sensitivity analysis, calibration among others), and the development of web-based repositories of designs for immediate application. While it is written at a level that is accessible to readers with Masters-level training in Statistics, the book is written in sufficient detail to be useful for practitioners and researchers. New to this revised and expanded edition: • An expanded presentation of basic material on computer experiments and Gaussian processes with additional simulations and examples • A new comparison of plug-in prediction methodologies for real-valued simulator output • An enlarged discussion of space-filling designs including Latin Hypercube designs (LHDs), near-orthogonal designs, and nonrectangular regions • A chapter length description of process-based designs for optimization, to improve good overall fit, quantile estimation, and Pareto optimization • A new chapter describing graphical and numerical sensitivity analysis tools • Substantial new material on calibration-based prediction and inference for calibration parameters • Lists of software that can be used to fit models discussed in the book to aid practitioners
Author | : Dariusz Ucinski |
Publisher | : Springer Science & Business Media |
Total Pages | : 254 |
Release | : 2013-03-21 |
Genre | : Mathematics |
ISBN | : 331900218X |
Download mODa 10 – Advances in Model-Oriented Design and Analysis Book in PDF, ePub and Kindle
This book collects the proceedings of the 10th Workshop on Model-Oriented Design and Analysis (mODa). A model-oriented view on the design of experiments, which is the unifying theme of all mODa meetings, assumes some knowledge of the form of the data-generating process and naturally leads to the so-called optimum experimental design. Its theory and practice have since become important in many scientific and technological fields, ranging from optimal designs for dynamic models in pharmacological research, to designs for industrial experimentation, to designs for simulation experiments in environmental risk management, to name but a few. The methodology has become even more important in recent years because of the increased speed of scientific developments, the complexity of the systems currently under investigation and the mounting pressure on businesses, industries and scientific researchers to reduce product and process development times. This increased competition requires ever increasing efficiency in experimentation, thus necessitating new statistical designs. This book presents a rich collection of carefully selected contributions ranging from statistical methodology to emerging applications. It primarily aims to provide an overview of recent advances and challenges in the field, especially in the context of new formulations, methods and state-of-the-art algorithms. The topics included in this volume will be of interest to all scientists and engineers and statisticians who conduct experiments.
Author | : Christos P. Kitsos |
Publisher | : Springer Science & Business Media |
Total Pages | : 104 |
Release | : 2014-01-09 |
Genre | : Mathematics |
ISBN | : 3642452876 |
Download Optimal Experimental Design for Non-Linear Models Book in PDF, ePub and Kindle
This book tackles the Optimal Non-Linear Experimental Design problem from an applications perspective. At the same time it offers extensive mathematical background material that avoids technicalities, making it accessible to non-mathematicians: Biologists, Medical Statisticians, Sociologists, Engineers, Chemists and Physicists will find new approaches to conducting their experiments. The book is recommended for Graduate Students and Researchers.
Author | : Joachim Kunert |
Publisher | : Springer |
Total Pages | : 256 |
Release | : 2016-06-06 |
Genre | : Mathematics |
ISBN | : 3319312669 |
Download mODa 11 - Advances in Model-Oriented Design and Analysis Book in PDF, ePub and Kindle
This volume contains pioneering contributions to both the theory and practice of optimal experimental design. Topics include the optimality of designs in linear and nonlinear models, as well as designs for correlated observations and for sequential experimentation. There is an emphasis on applications to medicine, in particular, to the design of clinical trials. Scientists from Europe, the US, Asia, Australia and Africa contributed to this volume of papers from the 11th Workshop on Model Oriented Design and Analysis.
Author | : Friedrich Pukelsheim |
Publisher | : SIAM |
Total Pages | : 527 |
Release | : 2006-04-01 |
Genre | : Mathematics |
ISBN | : 0898716047 |
Download Optimal Design of Experiments Book in PDF, ePub and Kindle
Optimal Design of Experiments offers a rare blend of linear algebra, convex analysis, and statistics. The optimal design for statistical experiments is first formulated as a concave matrix optimization problem. Using tools from convex analysis, the problem is solved generally for a wide class of optimality criteria such as D-, A-, or E-optimality. The book then offers a complementary approach that calls for the study of the symmetry properties of the design problem, exploiting such notions as matrix majorization and the Kiefer matrix ordering. The results are illustrated with optimal designs for polynomial fit models, Bayes designs, balanced incomplete block designs, exchangeable designs on the cube, rotatable designs on the sphere, and many other examples.
Author | : Peter Goos |
Publisher | : John Wiley & Sons |
Total Pages | : 249 |
Release | : 2011-06-28 |
Genre | : Science |
ISBN | : 1119976162 |
Download Optimal Design of Experiments Book in PDF, ePub and Kindle
"This is an engaging and informative book on the modern practice of experimental design. The authors' writing style is entertaining, the consulting dialogs are extremely enjoyable, and the technical material is presented brilliantly but not overwhelmingly. The book is a joy to read. Everyone who practices or teaches DOE should read this book." - Douglas C. Montgomery, Regents Professor, Department of Industrial Engineering, Arizona State University "It's been said: 'Design for the experiment, don't experiment for the design.' This book ably demonstrates this notion by showing how tailor-made, optimal designs can be effectively employed to meet a client's actual needs. It should be required reading for anyone interested in using the design of experiments in industrial settings." —Christopher J. Nachtsheim, Frank A Donaldson Chair in Operations Management, Carlson School of Management, University of Minnesota This book demonstrates the utility of the computer-aided optimal design approach using real industrial examples. These examples address questions such as the following: How can I do screening inexpensively if I have dozens of factors to investigate? What can I do if I have day-to-day variability and I can only perform 3 runs a day? How can I do RSM cost effectively if I have categorical factors? How can I design and analyze experiments when there is a factor that can only be changed a few times over the study? How can I include both ingredients in a mixture and processing factors in the same study? How can I design an experiment if there are many factor combinations that are impossible to run? How can I make sure that a time trend due to warming up of equipment does not affect the conclusions from a study? How can I take into account batch information in when designing experiments involving multiple batches? How can I add runs to a botched experiment to resolve ambiguities? While answering these questions the book also shows how to evaluate and compare designs. This allows researchers to make sensible trade-offs between the cost of experimentation and the amount of information they obtain.